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研究生: 林子予
Lin, Zih-Yu
論文名稱: 研發密度波動型態與企業社會責任效果對信用風險的影響
The Impact of R&D Density Volatility Pattern and CSR Effect on Credit Risk
指導教授: 林軒竹
Lin, Hsuan-Chu
學位類別: 碩士
Master
系所名稱: 管理學院 - 財務金融研究所
Graduate Institute of Finance
論文出版年: 2016
畢業學年度: 104
語文別: 英文
論文頁數: 81
中文關鍵詞: 企業社會責任信用風險債券利差研發支出研發密度標準差研發密度偏態研發密度峰態
外文關鍵詞: CSR, Credit Risk, Yield Spread, R&D Expenditure, Standard Deviation of R&D Density, Skewness of R&D Density, Kurtosis of R&D Density
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  • 本研究以「債權人」角度研究債券投資之信用風險評估,對債權人而言,企業「獲利能力」相當關鍵,其影響未來是否有能力償還本息。本研究有兩軸線,軸線一是「研發密度波動型態對信用風險之影響」,軸線二是「CSR效果如何影響信用風險」。兩者結合的特色在於,前者具影響短期獲利能力的作用,後者具短期獲利能力保護的作用,於本研究可同時一窺兩種樣貌對於短期獲利能力與短期償債能力的經濟意涵,進而探討兩者如何影響信用風險。

    軸線一:以往研究僅用研發密度標準差解釋研發密度波動型態。本研究加入了研發密度偏態與峰態作為解釋變數,實證結果顯示,研發密度偏態與峰態具顯著評價意義──研發密度偏態越大,代表研發支出易出現大額極端值,可能影響短期獲利與償債能力,導致信用風險越高;研發密度峰態越大,代表研發支出較易接近期望值,雖存在極端狀況,但發生機率非常低,使企業未來現金流量較容易被預期,假設其他情況不變,短期獲利與償債能力較不受影響,企業信用風險因此較小。然而,本研究中,研發密度標準差較無評價的貢獻。若研究中缺乏研發密度偏態與峰態,則無法周全解釋研發密度波動型態對信用風險的影響與探究背後更細緻的經濟意涵。由實證結果推測,債券投資人可能已將研發密度偏態與峰態的概念納入投資決策的風險評估中。

    軸線二:過去研究雖指出CSR影響信用風險的眾多原因,但缺乏有邏輯的推論說明CSR如何影響信用風險。本研究試推論CSR效果影響信用風險背後隱含的邏輯關係。研究中,先透過文獻整理,再提出影響邏輯,其中結合CSR廣告效果、道德資本與CSR類保險效果,此關係造就CSR影響信用風險的可能原因。實證結果發現,當企業於CSR做得好且有長期耕耘時,會顯著降低其信用風險。其也代表債券投資人可能已將CSR納入投資決策風險衡量中。

    There are two axes in my research: one is investigating “the impact of R&D density volatility pattern on credit risk”, and the other is inferring “how/why CSR effect affects credit risk”. Both axes are based on the perspective of bondholders for evaluating investment decision and risk management. For them, “profitability” is vital and can affect short-term profitability and liquidity. The former axis is related to “profitability”, and the latter axis is related to “protection for profitability”.

    With respect to “the impact of R&D density volatility pattern on credit risk”, past researchers only used the standard deviation of R&D density to represent R&D density volatility pattern. I add skewness and kurtosis of R&D density into explanatory variables, and I find that both significantly affect credit risk: the greater the skewness is, the greater the credit risk is. The economic meaning is that greater skewness means more potentially extremely large R&D expenditure, and then lower short-term profitability and higher liquidity risk; the greater the kurtosis is, the smaller the credit risk is. The economic meaning is that greater kurtosis represents more stable R&D expenditure, and then stable expected cash flow. Although extreme situation exists, these situations are quite few and could barely happen. Compared to past studies, the standard deviation of R&D density lacks of significance for credit risk evaluation in my research. Evidently, researches on related topic might not do without skewness and kurtosis. My empirical finding implies bondholders may have taken skewness and kurtosis of R&D density into account when they evaluate risk control for investment decision.

    Concerning “how/why CSR effect can affect credit risk”, past studies pointed out lots of reasons for why CSR can affect credit risk, but in lack of completely logical description for this issue. My study fills up this gap by step by step combining CSR advertising effect, moral capital, and CSR insurance-like effect from literature review to infer the implied logical relationship, which accounts for the possible reason why CSR effect can affect credit risk. The empirical finding shows that credit risk will significantly reduce if firms do CSR good in a basis of long-term continuity. My empirical result implies that bondholders may have taken CSR effect into consideration, when they evaluate the risk management issue for investment decision.

    摘要 I Abstract III Acknowledgement V Chapter 1 Introduction 1 1.1 Research Background and Motivations 1 1.2 Research Objectives and Major Findings 4 1.3 Research Contributions 5 1.4 Research Framework 9 Chapter 2 Literature Review and Hypothesis Development 11 2.1 Literature Review 11 2.2 Hypothesis Development 27 Chapter 3 Data and Methodology 32 3.1 Sample Selection and Description 32 3.2 Methodology 33 Chapter 4 Empirical Results 46 4.1 Descriptive Statistics 46 4.2 Multiple Regression Analysis 49 Chapter 5 Robustness Test 63 Chapter 6 Conclusions and Investment Implication 70 References 74

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